{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99e3b0a3-cd83-47c5-8029-c06d0ca6968e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "测试集大小: 10000\n",
      "Files already downloaded and verified\n",
      "训练集大小: 50000\n",
      "训练集样本数量: 40000\n",
      "验证集样本数量: 10000\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义标签字体大小\n",
    "label_size = 18  # 标签大小\n",
    "ticklabel_size = 14  # 刻度标签大小\n",
    "    \n",
    "# 定义数据的转换方式：将图像转化为Tensor\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),  # 将图像转换为Tensor\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 对图像进行归一化\n",
    "])\n",
    "\n",
    "# 加载CIFAR-10测试集，设置transform进行数据转换\n",
    "testset = torchvision.datasets.CIFAR10(root='./Data', train=False, download=True, transform=transform)\n",
    "print(f\"测试集大小: {len(testset)}\")\n",
    "\n",
    "# 加载CIFAR-10训练集，设置transform进行数据转换\n",
    "trainset = torchvision.datasets.CIFAR10(root='./Data', train=True, download=True, transform=transform)\n",
    "print(f\"训练集大小: {len(trainset)}\")\n",
    "\n",
    "# 设置训练集和验证集的比例\n",
    "tr_cv_rate = 0.8  # 训练集与验证集的比例\n",
    "\n",
    "# 创建一个列表来存储每个类的索引\n",
    "class_indices = [[] for _ in range(10)]  # CIFAR-10共有10个类别\n",
    "\n",
    "# 填充class_indices\n",
    "for idx, (_, label) in enumerate(trainset):\n",
    "    class_indices[label].append(idx)\n",
    "\n",
    "# 计算每个类别在训练集和验证集中的样本数量\n",
    "train_size_per_class = int(tr_cv_rate * min(len(indices) for indices in class_indices))  # 训练集每个类别的样本数量\n",
    "val_size_per_class = min(len(indices) for indices in class_indices) - train_size_per_class  # 验证集每个类别的样本数量\n",
    "\n",
    "# 创建平衡的训练集和验证集\n",
    "train_indices = []\n",
    "val_indices = []\n",
    "for indices in class_indices:\n",
    "    train_indices.extend(indices[:train_size_per_class])  # 训练集使用前tr_cv_rate比例的样本\n",
    "    val_indices.extend(indices[train_size_per_class:train_size_per_class + val_size_per_class])  # 验证集使用剩余样本\n",
    "\n",
    "# 创建Subset数据集\n",
    "from torch.utils.data import Subset\n",
    "trX = Subset(trainset, train_indices)  # 创建训练集子集\n",
    "cvX = Subset(trainset, val_indices)    # 创建验证集子集\n",
    "\n",
    "print(f\"训练集样本数量: {len(trX)}\")\n",
    "print(f\"验证集样本数量: {len(cvX)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0017f31-8e63-49dd-8136-4ddb29459374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图像的通道数: 3\n",
      "tensor([[0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
      "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
      "        [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
      "        [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# 设置批次大小\n",
    "batch_size = 42  # 定义训练批次大小\n",
    "\n",
    "# 自定义 collate_fn，进行 one-hot 编码\n",
    "def one_hot_collate(batch):\n",
    "    data = torch.stack([item[0] for item in batch])  # 提取数据部分\n",
    "    labels = torch.tensor([item[1] for item in batch])  # 提取标签部分\n",
    "    one_hot_labels = torch.zeros(labels.size(0), 10)  # CIFAR-10 有 10 类\n",
    "    one_hot_labels.scatter_(1, labels.unsqueeze(1), 1)  # 进行 one-hot 编码\n",
    "    return data, one_hot_labels\n",
    "\n",
    "# 加载CIFAR-10训练集、验证集和测试集，并应用自定义 collate_fn\n",
    "trLoader = torch.utils.data.DataLoader(trX, batch_size=batch_size, shuffle=True, num_workers=0, collate_fn=one_hot_collate)\n",
    "cvLoader = torch.utils.data.DataLoader(cvX, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=one_hot_collate)\n",
    "teLoader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0, collate_fn=one_hot_collate)\n",
    "\n",
    "# 获取一个批次的训练数据\n",
    "dataiter = iter(trLoader)\n",
    "data, labels = next(dataiter)\n",
    "\n",
    "# 获取图像通道数，对于CIFAR-10来说，应该是3通道（RGB）\n",
    "image_channels = data[0].numpy().shape[0]\n",
    "print(f'图像的通道数: {image_channels}')\n",
    "\n",
    "# 打印one-hot编码后的标签\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f32e4a8e-c89f-4b4b-b7a5-d2c52d238177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def imshow(img):\n",
    "    # 反标准化：将图像恢复到[0, 1]范围\n",
    "    img = img / 2 + 0.5  # 假设标准化时使用了 mean=[0.485, 0.456, 0.406] 和 std=[0.229, 0.224, 0.225]\n",
    "    npimg = img.numpy()\n",
    "    npimg = np.clip(npimg, 0, 1)  # 确保值在[0, 1]范围内\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "# 获取一批图像\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# 显示图像\n",
    "imshow(torchvision.utils.make_grid(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51ce5466-420a-4e08-843f-9616f5296ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=4096, out_features=100, bias=True)\n",
      "  (fc2): Linear(in_features=100, out_features=10, bias=True)\n",
      "  (softmax): Softmax(dim=1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# 定义卷积神经网络\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, image_channels, num_classes):\n",
    "        super(CNN, self).__init__()\n",
    "        \n",
    "        # 第一个卷积层\n",
    "        self.conv1 = nn.Conv2d(in_channels=image_channels, out_channels=32, kernel_size=3, padding=1)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        \n",
    "        # 第二个卷积层\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        \n",
    "        # 全连接层\n",
    "        self.fc1 = nn.Linear(64 * 8 * 8, 100)  # CIFAR-10 图片大小为 32x32，经过两次 2x2 最大池化后，尺寸变为 8x8\n",
    "        self.fc2 = nn.Linear(100, num_classes)  # 最终输出 10 类\n",
    "\n",
    "        # Softmax\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 卷积 + 激活 + 池化\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.pool1(x)\n",
    "        \n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.pool2(x)\n",
    "        \n",
    "        # 展平输出\n",
    "        x = x.view(-1, 64 * 8 * 8)\n",
    "        \n",
    "        # 全连接层\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        \n",
    "        # Softmax\n",
    "        x = self.softmax(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "# 初始化模型\n",
    "image_channels = 3  # CIFAR-10 为 3 通道（RGB）\n",
    "model = CNN(image_channels, 10)\n",
    "\n",
    "# 判断是否有 GPU 可用\n",
    "if torch.cuda.is_available():\n",
    "    model = model.cuda()\n",
    "\n",
    "# 显示模型架构\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc8a85b0-b516-4cfe-9586-b18d594dfeb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Train Loss: 2.0725, CV Loss: 2.0116\n",
      "Epoch [2/50], Train Loss: 1.9468, CV Loss: 1.9350\n",
      "Epoch [3/50], Train Loss: 1.8854, CV Loss: 1.8708\n",
      "Epoch [4/50], Train Loss: 1.8407, CV Loss: 1.8345\n",
      "Epoch [5/50], Train Loss: 1.8089, CV Loss: 1.8372\n",
      "Epoch [6/50], Train Loss: 1.7834, CV Loss: 1.8281\n",
      "Epoch [7/50], Train Loss: 1.7621, CV Loss: 1.7989\n",
      "Epoch [8/50], Train Loss: 1.7430, CV Loss: 1.8023\n",
      "Epoch [9/50], Train Loss: 1.7279, CV Loss: 1.7880\n",
      "Epoch [10/50], Train Loss: 1.7135, CV Loss: 1.7857\n",
      "Epoch [11/50], Train Loss: 1.7001, CV Loss: 1.7733\n",
      "Epoch [12/50], Train Loss: 1.6860, CV Loss: 1.7881\n",
      "Epoch [13/50], Train Loss: 1.6772, CV Loss: 1.7729\n",
      "Epoch [14/50], Train Loss: 1.6675, CV Loss: 1.7696\n",
      "Epoch [15/50], Train Loss: 1.6616, CV Loss: 1.7683\n",
      "Epoch [16/50], Train Loss: 1.6542, CV Loss: 1.7586\n",
      "Epoch [17/50], Train Loss: 1.6438, CV Loss: 1.7725\n",
      "Epoch [18/50], Train Loss: 1.6396, CV Loss: 1.7611\n",
      "Epoch [19/50], Train Loss: 1.6344, CV Loss: 1.7598\n",
      "Epoch [20/50], Train Loss: 1.6275, CV Loss: 1.7643\n",
      "Epoch [21/50], Train Loss: 1.6248, CV Loss: 1.7648\n",
      "Epoch [22/50], Train Loss: 1.6214, CV Loss: 1.7617\n",
      "Epoch [23/50], Train Loss: 1.6160, CV Loss: 1.7628\n",
      "Epoch [24/50], Train Loss: 1.6124, CV Loss: 1.7697\n",
      "Epoch [25/50], Train Loss: 1.6123, CV Loss: 1.7650\n",
      "Epoch [26/50], Train Loss: 1.6074, CV Loss: 1.7628\n",
      "Epoch [27/50], Train Loss: 1.6059, CV Loss: 1.7769\n",
      "Epoch [28/50], Train Loss: 1.6048, CV Loss: 1.7602\n",
      "Epoch [29/50], Train Loss: 1.6035, CV Loss: 1.7665\n",
      "Epoch [30/50], Train Loss: 1.6004, CV Loss: 1.7713\n",
      "Epoch [31/50], Train Loss: 1.6025, CV Loss: 1.7682\n",
      "Epoch [32/50], Train Loss: 1.5962, CV Loss: 1.7679\n",
      "Epoch [33/50], Train Loss: 1.5944, CV Loss: 1.7661\n",
      "Epoch [34/50], Train Loss: 1.5973, CV Loss: 1.7686\n",
      "Epoch [35/50], Train Loss: 1.5931, CV Loss: 1.7699\n",
      "Epoch [36/50], Train Loss: 1.5937, CV Loss: 1.7674\n",
      "Epoch [37/50], Train Loss: 1.5904, CV Loss: 1.7664\n",
      "Epoch [38/50], Train Loss: 1.5857, CV Loss: 1.7672\n",
      "Epoch [39/50], Train Loss: 1.5851, CV Loss: 1.7627\n",
      "Epoch [40/50], Train Loss: 1.5859, CV Loss: 1.7657\n",
      "Epoch [41/50], Train Loss: 1.5861, CV Loss: 1.7716\n",
      "Epoch [42/50], Train Loss: 1.5865, CV Loss: 1.7679\n",
      "Epoch [43/50], Train Loss: 1.5889, CV Loss: 1.7725\n",
      "Epoch [44/50], Train Loss: 1.5819, CV Loss: 1.7701\n",
      "Epoch [45/50], Train Loss: 1.5818, CV Loss: 1.7649\n",
      "Epoch [46/50], Train Loss: 1.5829, CV Loss: 1.7747\n",
      "Epoch [47/50], Train Loss: 1.5806, CV Loss: 1.7738\n",
      "Epoch [48/50], Train Loss: 1.5833, CV Loss: 1.7672\n",
      "Epoch [49/50], Train Loss: 1.5795, CV Loss: 1.7679\n",
      "Epoch [50/50], Train Loss: 1.5810, CV Loss: 1.7670\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()  # 使用交叉熵损失\n",
    "optimizer = torch.optim.Adam(model.parameters())  # 使用Adam优化器\n",
    "\n",
    "# 用于存储损失的列表\n",
    "train_losses = []\n",
    "cv_losses = []\n",
    "\n",
    "# 训练轮次\n",
    "num_epochs = 50\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # 设置模型为训练模式\n",
    "    batch_losses = []  # 存储每个批次的损失\n",
    "    \n",
    "    # 遍历训练数据加载器\n",
    "    for batch_x, batch_y in trLoader:\n",
    "        # 将数据转移到GPU（如果可用）\n",
    "        batch_x, batch_y = batch_x.cuda() if torch.cuda.is_available() else batch_x, batch_y.cuda() if torch.cuda.is_available() else batch_y\n",
    "        \n",
    "        # 正向传播\n",
    "        outputs = model(batch_x)\n",
    "        loss = criterion(outputs, batch_y)\n",
    "        \n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()  # 清空之前的梯度\n",
    "        loss.backward()  # 计算梯度\n",
    "        optimizer.step()  # 更新参数\n",
    "        \n",
    "        batch_losses.append(loss.item())  # 保存当前批次的损失\n",
    "    \n",
    "    # 计算当前 epoch 的平均训练损失\n",
    "    avg_train_loss = sum(batch_losses) / len(batch_losses)\n",
    "    train_losses.append(avg_train_loss)\n",
    "    \n",
    "    # 在验证集上进行评估\n",
    "    model.eval()  # 设置模型为评估模式\n",
    "    cv_batch_losses = []  # 存储验证集每个批次的损失\n",
    "    with torch.no_grad():  # 评估阶段不需要计算梯度\n",
    "        for cv_x, cv_y in cvLoader:\n",
    "            # 将数据转移到GPU（如果可用）\n",
    "            cv_x, cv_y = cv_x.cuda() if torch.cuda.is_available() else cv_x, cv_y.cuda() if torch.cuda.is_available() else cv_y\n",
    "            \n",
    "            cv_outputs = model(cv_x)\n",
    "            cv_loss = criterion(cv_outputs, cv_y)\n",
    "            cv_batch_losses.append(cv_loss.item())\n",
    "    \n",
    "    # 计算当前 epoch 的验证损失\n",
    "    avg_cv_loss = sum(cv_batch_losses) / len(cv_batch_losses)\n",
    "    cv_losses.append(avg_cv_loss)\n",
    "    \n",
    "    # 输出每个epoch的训练损失和验证损失\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {avg_train_loss:.4f}, CV Loss: {avg_cv_loss:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4c0c509-dce7-4eaf-b234-5ec7ef01a9ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 87.68%\n",
      "Accuracy on cross-validation set: 69.25%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 评估模型在训练集和交叉验证集上的准确率\n",
    "model.eval()  # 设置模型为评估模式\n",
    "with torch.no_grad():  # 不计算梯度\n",
    "    # 计算训练集准确率\n",
    "    tr_correct = 0\n",
    "    tr_total = 0\n",
    "    for images, labels in trLoader:\n",
    "        # 将数据转移到GPU（如果可用）\n",
    "        images, labels = images.cuda() if torch.cuda.is_available() else images, labels.cuda() if torch.cuda.is_available() else labels\n",
    "        \n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs, 1)  # 获取每个样本的预测类别\n",
    "        _, true_labels = torch.max(labels, 1)  # 获取真实类别\n",
    "        tr_total += labels.size(0)\n",
    "        tr_correct += (predicted == true_labels).sum().item()\n",
    "    \n",
    "    tr_accuracy = 100 * tr_correct / tr_total  # 训练集准确率\n",
    "\n",
    "    # 计算交叉验证集准确率\n",
    "    cv_correct = 0\n",
    "    cv_total = 0\n",
    "    for images, labels in cvLoader:\n",
    "        # 将数据转移到GPU（如果可用）\n",
    "        images, labels = images.cuda() if torch.cuda.is_available() else images, labels.cuda() if torch.cuda.is_available() else labels\n",
    "        \n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs, 1)  # 获取每个样本的预测类别\n",
    "        _, true_labels = torch.max(labels, 1)  # 获取真实类别\n",
    "        cv_total += labels.size(0)\n",
    "        cv_correct += (predicted == true_labels).sum().item()\n",
    "    \n",
    "    cv_accuracy = 100 * cv_correct / cv_total  # 交叉验证集准确率\n",
    "\n",
    "# 输出准确率\n",
    "print(f'Accuracy on training set: {tr_accuracy:.2f}%')\n",
    "print(f'Accuracy on cross-validation set: {cv_accuracy:.2f}%')\n",
    "\n",
    "# 绘制训练损失和交叉验证损失的变化曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(range(1, num_epochs+1), train_losses, label='Training Loss')\n",
    "plt.plot(range(1, num_epochs+1), cv_losses, label='Cross-Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Cross-Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e92ee823-0d57-47d9-9356-1cad41b1e86e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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